What's the message? A content analysis of newspaper articles about assisted reproductive technology from 2005 to 2011
Bibliographic record
Abstract
Infertility and its treatment is the subject of considerable media coverage. In order to evaluate the representation of assisted reproductive technology (ART) in the popular media, we conducted a content analysis of North American newspaper articles. We also explored whether different themes emerged in relation to the implementation of public funding for ART in Quebec, Canada. Print and online newspaper articles from 2005 to 2011 were retrieved using the terms "in-vitro fertilization", "infertility treatment", "assisted reproductive technology", and "IVF treatment". Totally, 719 newspaper articles met inclusion criteria and were coded according to predetermined categories. Risks (63%) and ethical issues (61%) related to ART were most commonly featured. Quebec-based articles were mostly concerned with the politics and financial issues governing ART, and were less likely to report the risks and emotional impact of ART than other North American press. Newspapers tended to emphasize extreme scenarios as well as controversial cases that may not represent the everyday realities of ART. Changes in public policy may also engender shifts in the tone and content of media reports. It is important to establish resources that can inform the public as well as prospective infertility patients about their condition and potential treatment options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".